The present invention relates to a method for characterizing an image based on the color or textural content of the image.
Image characterization is a process for describing an image based upon the outcomes of the application of preselected measures to the image. Image characterization is useful in a number of applications such as digital image libraries where image content is useful as a basis for image indexing and retrieval. For image characterization to be practical and effective the outcome of the application of the measures to the image should be: (1) sufficient to distinguish between different images, (2) invariant to certain types of transformations of the image, (3) insensitive to noise, (4) easy to compute and (5) compact. Various methods of image characterization have been used and proposed with resulting image descriptors exhibiting these attributes to differing degrees.
A paper by Swain et al. entitled COLOR INDEXING describes the use of color histograms to characterize images. A color histogram of an image is obtained by calculating the frequency distribution of picture elements or pixels as a function of pixel color. Color histograms are invariant to translation or rotation of the image about the viewing axis. Color histograms can differ markedly for images with differing features. However, all spatial information about the features in the image is discarded in the creation of the color histogram. Therefore as long as two images have the same number of picture elements of each color it is not possible to distinguish between them using color histograms. This is true even if the two images contain features of completely different size or shape. For example, the total areas of the like colored (like hatched) geometric features of the two images of FIG. 1A and FIG. 1B are equal and require the same number of picture elements. The images cannot be distinguished on the basis of their color histograms even though the features are clearly very different in size and number, and the images are easily distinguishable by the human eye.
Several methods have been proposed to improve different aspects of the performance of color histograms. Stricker et al. in the paper entitled SIMILARITY OF COLOR IMAGES proposed the use of color moments. Color moments are statistical measures of the shape and position of the population distribution of pixel colors. In particular the color moments include a mean, a standard deviation and a skewness. Expressing the information contained in the color histogram in terms of a color moment results in a very compact image descriptor. Funt et al. in the paper entitled COLOR CONSTANT COLOR INDEXING proposed using the ratios of color triples [the red, the green and the blue pixels (RGB)] from neighboring regions of an image to reduce the effects of intensity variations. Rubner et al. in the paper entitled NAVIGATING THROUGH A SPACE OF COLOR IMAGES proposed the use of color signatures which is a plot of clusters of similar colors in an RGB color space. Using color signatures reduces the amount of data necessary to describe an image compared to that required for a color histogram. These methods improve some aspects of the performance of the image descriptors over the color histogram. However, like the color histogram, no spatial information is preserved.
Several processes have been proposed which attempt to preserve some of the spatial information that is discarded in the construction of a color histogram. Pass et.al in the paper entitled HISTOGRAM REFINEMENT FOR CONTENT BASED IMAGE RETRIEVAL proposed refining the color histogram with color coherence vectors. In this process the coherence of the color of a picture element in relation to that of other picture elements in a contiguous region is determined. Even though the number of picture elements of each color is equal and, therefore, the color histograms are identical for two images, differences between features in the images will mean that the numbers of picture elements of each color which are color coherent will vary. Color coherence vectors do embed some spatial information in the descriptors. Unfortunately, they require at least twice as much additional storage space as a traditional histogram.
Rickman et al. in the paper entitled CONTENT-BASED IMAGE RETRIEVAL USING COLOUR TUPLE HISTOGRAMS proposed image characterization by construction of a histogram of the color hue at the vertices of randomly located triangular color tuples. Since the vertices of the triangular tuples are spaced apart, some spatial information is retained. Unfortunately, it is difficult to determine the dominant color of an image from the color tuple data. Further, the retained spatial information is difficult to interpret in a normal sense, therefore making it difficult to use the information for indexing an image database.
“Color correlograms” were proposed for image characterization by Huang et al. in the paper entitled IMAGE INDEXING USING COLOR CORRELOGRAMS. A color correlogram quantifies the probability that a pixel of a particular color will lie at a specified radial distance from a pixel of a particular color in the image. The color correlogram provides a technique of measuring color coherence at different scales or distances from a point on the image. However, it is difficult to determine the dominant color of the image from a correlogram and it is difficult to interpret the correlogram in any usual human sense.
Smith et al. in the paper entitled QUERYING BY COLOR REGIONS USING THE VISUALSEEK CONTENT-BASED VISUAL QUERY SYSTEM describes a method of image characterization using regions of color. Color data is transformed and the colors of the image are quantized and then filtered to emphasize prominent color regions. “Color set” values are extracted and a histogram is approximated by retaining those color set values above a threshold level. This method of image characterization requires image segmentation, a process that is difficult and computationally intensive. The region representation is rigid and variant to rotation or translation of images.
“Blobworld” is a method of image representation proposed by Carson et al. in the paper entitled REGION-BASED IMAGE QUERYING. In this method the image is segmented into a set of localized coherent regions of color and texture, known as “blobs.” The “blobworld” representation of the image is the result of recording the location, size, and color of the segmented color blobs. This method provides considerable spatial information about the image, but the “blobworld” representation is rigid and variant to rotation or translation of images. Further, the image segmentation process is difficult and requires substantial computational resources.
What is desired, therefore, is a method for characterizing an image that embeds sufficient spatial information in the descriptors to enable distinguishing images on the basis of their content. In addition the method should also be invariant to certain types of transformations of the image, conserve computational and storage resources, be insensitive to noise, and be easy to interpret in a normal sense.